numpy从np数组中删除维度 [英] Numpy remove a dimension from np array

问题描述

我有一些要处理的图像，问题是有两种图像均为106 x 106像素，有些是彩色的，有些是黑白的.

I have some images I want to work with, the problem is that there are two kinds of images both are 106 x 106 pixels, some are in color and some are black and white.

只有两(2)个维度的一个:

one with only two (2) dimensions:

(106,106)

一三(3)

(106,106,3)

(106,106,3)

有没有办法去除最后一个尺寸?

Is there a way I can strip this last dimension?

我尝试了np.delete，但似乎没有用.

I tried np.delete, but it did not seem to work.

np.shape(np.delete(Xtrain[0], [2] , 2))
Out[67]: (106, 106, 2)

推荐答案

您可以使用numpy的精美索引(Python内置切片符号的扩展):

You could use numpy's fancy indexing (an extension to Python's built-in slice notation):

x = np.zeros( (106, 106, 3) )
result = x[:, :, 0]
print(result.shape)

打印

(106, 106)

(106, 106, 3)的形状表示您有3套形状为(106, 106)的东西.因此，为了剥离"最后一个维度，您只需选择其中一个(这就是精美索引的功能).

A shape of (106, 106, 3) means you have 3 sets of things that have shape (106, 106). So in order to "strip" the last dimension, you just have to pick one of these (that's what the fancy indexing does).

您可以保留所需的任何切片.由于您未指定想要的内容，因此我任意选择保留0.因此，result = x[:, :, 1]和result = x[:, :, 2]也会提供所需的形状:这都取决于您需要保留哪个切片.

You can keep any slice you want. I arbitrarily choose to keep the 0th, since you didn't specify what you wanted. So, result = x[:, :, 1] and result = x[:, :, 2] would give the desired shape as well: it all just depends on which slice you need to keep.

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